SIMILARITY EVALUATION OF ONLINE SIGNATURES BASED ON MODIFIED DYNAMIC TIME WARPING

Many people are very accustomed to the process of signing their name and having it matched for authentication. In a signature verification system, the signatures are processed to extract features that are used for verification. These features should not be duplicable. A basic problem is intraclass variations that will greatly affect the matching scores produced. The problem of distinctiveness occurs when the expectation of signatures to vary significantly between individuals is not met. There may be a large number of similarities in the feature sets used to represent the signatures of two different individuals. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features used in the system. This study evaluates 40 functional features of viewpoint classification error and consistency for extracting the best subset once a set of features provides maximal discrimination capability between genuine and forged signatures. A modified distance of the DTW algorithm is proposed to improve performance of the verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity based. Second, the average EER for the proposed algorithm in comparison with the general DTW algorithm shows a 5.47% decrease. Moreover, a comparative study based on a different classifier with a skilled forgery shows that the best result has an EER of 1.73% using the Parzen window classifier.

[1]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[3]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[4]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Chulhan Lee,et al.  Online signature verification using temporal shift estimated by the phase of Gabor filter , 2005, IEEE Trans. Signal Process..

[6]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[7]  C. N. Liu,et al.  Automatic signature verification based on accelerometry , 1977 .

[8]  Anil K. Jain,et al.  Fusion of Local and Regional Approaches for On-Line Signature Verification , 2005, IWBRS.

[9]  Toby Berger,et al.  Reliable On-Line Human Signature Verification Systems , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Lei Hu,et al.  On-line signature verification based on fusion of global and local information , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[12]  Venu Govindaraju,et al.  A comparative study on the consistency of features in on-line signature verification , 2005, Pattern Recognit. Lett..

[13]  Khalid Saeed,et al.  Online Signature Classification and its Verification System , 2008, 2008 7th Computer Information Systems and Industrial Management Applications.

[14]  Bin Li,et al.  On-line signature verification for e-finance and e-commerce security system , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[15]  Daisuke Sakamoto,et al.  Dynamic biometric person authentication using pen signature trajectories , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[16]  Ho-Sub Yoon,et al.  Using geometric extrema for segment-to-segment characteristics comparison in online signature verification , 2004, Pattern Recognit..

[17]  Bernhard Sick,et al.  Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Anil K. Jain,et al.  On-line signature verification, , 2002, Pattern Recognit..

[19]  Hong Chang,et al.  SVC2004: First International Signature Verification Competition , 2004, ICBA.

[20]  Venu Govindaraju,et al.  ER2: an intuitive similarity measure for on-line signature verification , 2004, IWFHR.

[21]  Loris Nanni,et al.  Ensemble of Parzen window classifiers for on-line signature verification , 2005, Neurocomputing.

[22]  Emile H. L. Aarts,et al.  On-line signature verification with hidden Markov models , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).